






import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 							snake_case_       (    unittest.TestCase      ):

						"""simple docstring"""



						@property
						def        UpperCAmelCase__					(self:					Any   )    ->     Optional[Any]:




											'''simple docstring'''



											torch.manual_seed(0   )
											__a					:						Dict      =					UNetaDModel(
											    block_out_channels=(32, 64) ,					layers_per_block=2 ,					sample_size=32 ,					in_channels=3 ,					out_channels=3 ,					down_block_types=("DownBlock2D", "AttnDownBlock2D") ,					up_block_types=("AttnUpBlock2D", "UpBlock2D") ,					)
											return model



						def        UpperCAmelCase__					(self:					Tuple   )    ->     List[Any]:




											'''simple docstring'''



											__a					:						Any      =					self.dummy_uncond_unet
											__a					:						Tuple      =					KarrasVeScheduler()

											__a					:						Dict      =					KarrasVePipeline(unet=__UpperCAmelCase ,					scheduler=__UpperCAmelCase   )
											pipe.to(__UpperCAmelCase   )
											pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

											__a					:						str      =					torch.manual_seed(0   )
											__a					:						Any      =					pipe(num_inference_steps=2 ,					generator=__UpperCAmelCase ,					output_type="numpy"   ).images

											__a					:						List[Any]      =					torch.manual_seed(0   )
											__a					:						Optional[int]      =					pipe(num_inference_steps=2 ,					generator=__UpperCAmelCase ,					output_type="numpy" ,					return_dict=__UpperCAmelCase   )[0]

											__a					:						Union[str, Any]      =					image[0, -3:, -3:, -1]
											__a					:						List[Any]      =					image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											__a					:						str      =					np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

											assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2




@slow
@require_torch
class 							snake_case_       (    unittest.TestCase      ):

						"""simple docstring"""



						def        UpperCAmelCase__					(self:					Dict   )    ->     Tuple:




											'''simple docstring'''



											__a					:						Any      =					'''google/ncsnpp-celebahq-256'''
											__a					:						Optional[int]      =					UNetaDModel.from_pretrained(__UpperCAmelCase   )
											__a					:						Optional[int]      =					KarrasVeScheduler()

											__a					:						List[str]      =					KarrasVePipeline(unet=__UpperCAmelCase ,					scheduler=__UpperCAmelCase   )
											pipe.to(__UpperCAmelCase   )
											pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

											__a					:						Any      =					torch.manual_seed(0   )
											__a					:						List[str]      =					pipe(num_inference_steps=20 ,					generator=__UpperCAmelCase ,					output_type="numpy"   ).images

											__a					:						Dict      =					image[0, -3:, -3:, -1]
											assert image.shape == (1, 256, 256, 3)
											__a					:						Any      =					np.array([0.5_78, 0.58_11, 0.59_24, 0.58_09, 0.5_87, 0.58_86, 0.58_61, 0.58_02, 0.5_86]   )

											assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2








import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


lowercase			:				Optional[int]   =     logging.get_logger(__name__)

lowercase			:				Optional[Any]   =     {"vocab_file": "vocab.txt"}

lowercase			:				Optional[int]   =     {
    "vocab_file": {
        "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt",
        "YituTech/conv-bert-medium-small": (
            "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt"
        ),
        "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt",
    }
}

lowercase			:				Tuple   =     {
    "YituTech/conv-bert-base": 5_12,
    "YituTech/conv-bert-medium-small": 5_12,
    "YituTech/conv-bert-small": 5_12,
}


lowercase			:				Optional[int]   =     {
    "YituTech/conv-bert-base": {"do_lower_case": True},
    "YituTech/conv-bert-medium-small": {"do_lower_case": True},
    "YituTech/conv-bert-small": {"do_lower_case": True},
}







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		int								=	VOCAB_FILES_NAMES
							UpperCAmelCase_						:		Optional[int]								=	PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase_						:		List[Any]								=	PRETRAINED_INIT_CONFIGURATION
							UpperCAmelCase_						:		Optional[Any]								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase_						:		Optional[int]								=	ConvBertTokenizer




							def __init__(					self    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase="[UNK]"    ,						__UpperCAmelCase="[SEP]"    ,						__UpperCAmelCase="[PAD]"    ,						__UpperCAmelCase="[CLS]"    ,						__UpperCAmelCase="[MASK]"    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase=None    ,						**__UpperCAmelCase    ,						)		->       Optional[int]:
														super().__init__(
														    __UpperCAmelCase    ,						tokenizer_file=__UpperCAmelCase    ,						do_lower_case=__UpperCAmelCase    ,						unk_token=__UpperCAmelCase    ,						sep_token=__UpperCAmelCase    ,						pad_token=__UpperCAmelCase    ,						cls_token=__UpperCAmelCase    ,						mask_token=__UpperCAmelCase    ,						tokenize_chinese_chars=__UpperCAmelCase    ,						strip_accents=__UpperCAmelCase    ,						**__UpperCAmelCase    ,						)

														A      :					Any      		=					json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
														if (
														    normalizer_state.get('''lowercase'''    ,						__UpperCAmelCase						) != do_lower_case
														    or normalizer_state.get('''strip_accents'''    ,						__UpperCAmelCase						) != strip_accents
														    or normalizer_state.get('''handle_chinese_chars'''    ,						__UpperCAmelCase						) != tokenize_chinese_chars
														):
																					A      :					Tuple      		=					getattr(__UpperCAmelCase    ,						normalizer_state.pop('''type'''						)						)
																					A      :					int      		=					do_lower_case
																					A      :					int      		=					strip_accents
																					A      :					Union[str, Any]      		=					tokenize_chinese_chars
																					A      :					str      		=					normalizer_class(**__UpperCAmelCase						)

														A      :					str      		=					do_lower_case




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=None						)		->       Optional[int]:
														A      :					Dict      		=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

														if token_ids_a:
																					output += token_ids_a + [self.sep_token_id]

														return output




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None						)		->       List[int]:
														A      :					List[Any]      		=					[self.sep_token_id]
														A      :					Optional[Any]      		=					[self.cls_token_id]
														if token_ids_a is None:
																					return len(cls + token_ids_a + sep						) * [0]
														return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None						)		->       Tuple[str]:
														A      :					Optional[int]      		=					self._tokenizer.model.save(__UpperCAmelCase    ,						name=__UpperCAmelCase						)
														return tuple(__UpperCAmelCase						)






